bfi.Rd25 personality self report items taken from the International Personality Item Pool (ipip.ori.org) were included as part of the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project. The data from 2800 subjects are included here as a demonstration set for scale construction, factor analysis, and Item Response Theory analysis. Three additional demographic variables (sex, education, and age) are also included.
A data frame with 2800 observations on the following 28 variables. (The q numbers are the SAPA item numbers).
A1Am indifferent to the feelings of others. (q_146)
A2Inquire about others' well-being. (q_1162)
A3Know how to comfort others. (q_1206)
A4Love children. (q_1364)
A5Make people feel at ease. (q_1419)
C1Am exacting in my work. (q_124)
C2Continue until everything is perfect. (q_530)
C3Do things according to a plan. (q_619)
C4Do things in a half-way manner. (q_626)
C5Waste my time. (q_1949)
E1Don't talk a lot. (q_712)
E2Find it difficult to approach others. (q_901)
E3Know how to captivate people. (q_1205)
E4Make friends easily. (q_1410)
E5Take charge. (q_1768)
N1Get angry easily. (q_952)
N2Get irritated easily. (q_974)
N3Have frequent mood swings. (q_1099
N4Often feel blue. (q_1479)
N5Panic easily. (q_1505)
O1Am full of ideas. (q_128)
O2Avoid difficult reading material.(q_316)
O3Carry the conversation to a higher level. (q_492)
O4Spend time reflecting on things. (q_1738)
O5Will not probe deeply into a subject. (q_1964)
genderMales = 1, Females =2
education1 = HS, 2 = finished HS, 3 = some college, 4 = college graduate 5 = graduate degree
ageage in years
The first 25 items are organized by five putative factors: Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Opennness. The scoring key is created using make.keys, the scores are found using score.items.
These five factors are a useful example of using irt.fa to do Item Response Theory based latent factor analysis of the polychoric correlation matrix. The endorsement plots for each item, as well as the item information functions reveal that the items differ in their quality.
The item data were collected using a 6 point response scale: 1 Very Inaccurate 2 Moderately Inaccurate 3 Slightly Inaccurate 4 Slightly Accurate 5 Moderately Accurate 6 Very Accurate
as part of the Synthetic Apeture Personality Assessment (SAPA https://www.sapa-project.org/) project. To see an example of the data collection technique, visit https://www.SAPA-project.org/ or the International Cognitive Ability Resource at https://icar-project.org. The items given were sampled from the International Personality Item Pool of Lewis Goldberg using the sampling technique of SAPA. This is a sample data set taken from the much larger SAPA data bank.
The items are from the ipip (Goldberg, 1999). The data are from the SAPA project (Revelle, Wilt and Rosenthal, 2010) , collected Spring, 2010 ( https://www.sapa-project.org/).
Goldberg, L.R. (1999) A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. In Mervielde, I. and Deary, I. and De Fruyt, F. and Ostendorf, F. (eds) Personality psychology in Europe. 7. Tilburg University Press. Tilburg, The Netherlands.
Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.
Revelle, W, Condon, D.M., Wilt, J., French, J.A., Brown, A., and Elleman, L.G. (2016) Web and phone based data collection using planned missing designs. In Fielding, N.G., Lee, R.M. and Blank, G. (Eds). SAGE Handbook of Online Research Methods (2nd Ed), Sage Publcations.
The bfi data set and items should not be confused with the BFI (Big Five Inventory) of Oliver John and colleagues (John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big Five Inventory–Versions 4a and 54. Berkeley, CA: University of California,Berkeley, Institute of Personality and Social Research.)
data(bfi)
psych::describe(bfi)
#> vars n mean sd median trimmed mad min max range skew
#> A1 1 2784 2.41 1.41 2 2.23 1.48 1 6 5 0.83
#> A2 2 2773 4.80 1.17 5 4.98 1.48 1 6 5 -1.12
#> A3 3 2774 4.60 1.30 5 4.79 1.48 1 6 5 -1.00
#> A4 4 2781 4.70 1.48 5 4.93 1.48 1 6 5 -1.03
#> A5 5 2784 4.56 1.26 5 4.71 1.48 1 6 5 -0.85
#> C1 6 2779 4.50 1.24 5 4.64 1.48 1 6 5 -0.85
#> C2 7 2776 4.37 1.32 5 4.50 1.48 1 6 5 -0.74
#> C3 8 2780 4.30 1.29 5 4.42 1.48 1 6 5 -0.69
#> C4 9 2774 2.55 1.38 2 2.41 1.48 1 6 5 0.60
#> C5 10 2784 3.30 1.63 3 3.25 1.48 1 6 5 0.07
#> E1 11 2777 2.97 1.63 3 2.86 1.48 1 6 5 0.37
#> E2 12 2784 3.14 1.61 3 3.06 1.48 1 6 5 0.22
#> E3 13 2775 4.00 1.35 4 4.07 1.48 1 6 5 -0.47
#> E4 14 2791 4.42 1.46 5 4.59 1.48 1 6 5 -0.82
#> E5 15 2779 4.42 1.33 5 4.56 1.48 1 6 5 -0.78
#> N1 16 2778 2.93 1.57 3 2.82 1.48 1 6 5 0.37
#> N2 17 2779 3.51 1.53 4 3.51 1.48 1 6 5 -0.08
#> N3 18 2789 3.22 1.60 3 3.16 1.48 1 6 5 0.15
#> N4 19 2764 3.19 1.57 3 3.12 1.48 1 6 5 0.20
#> N5 20 2771 2.97 1.62 3 2.85 1.48 1 6 5 0.37
#> O1 21 2778 4.82 1.13 5 4.96 1.48 1 6 5 -0.90
#> O2 22 2800 2.71 1.57 2 2.56 1.48 1 6 5 0.59
#> O3 23 2772 4.44 1.22 5 4.56 1.48 1 6 5 -0.77
#> O4 24 2786 4.89 1.22 5 5.10 1.48 1 6 5 -1.22
#> O5 25 2780 2.49 1.33 2 2.34 1.48 1 6 5 0.74
#> gender 26 2800 1.67 0.47 2 1.71 0.00 1 2 1 -0.73
#> education 27 2577 3.19 1.11 3 3.22 1.48 1 5 4 -0.05
#> age 28 2800 28.78 11.13 26 27.43 10.38 3 86 83 1.02
#> kurtosis se
#> A1 -0.31 0.03
#> A2 1.05 0.02
#> A3 0.44 0.02
#> A4 0.04 0.03
#> A5 0.16 0.02
#> C1 0.30 0.02
#> C2 -0.14 0.03
#> C3 -0.13 0.02
#> C4 -0.62 0.03
#> C5 -1.22 0.03
#> E1 -1.09 0.03
#> E2 -1.15 0.03
#> E3 -0.47 0.03
#> E4 -0.30 0.03
#> E5 -0.09 0.03
#> N1 -1.01 0.03
#> N2 -1.05 0.03
#> N3 -1.18 0.03
#> N4 -1.09 0.03
#> N5 -1.06 0.03
#> O1 0.43 0.02
#> O2 -0.81 0.03
#> O3 0.30 0.02
#> O4 1.08 0.02
#> O5 -0.24 0.03
#> gender -1.47 0.01
#> education -0.32 0.02
#> age 0.56 0.21
# create the bfi.keys (actually already saved in the data file)
bfi.keys <-
list(agree=c("-A1","A2","A3","A4","A5"),conscientious=c("C1","C2","C3","-C4","-C5"),
extraversion=c("-E1","-E2","E3","E4","E5"),neuroticism=c("N1","N2","N3","N4","N5"),
openness = c("O1","-O2","O3","O4","-O5"))
scores <- psych::scoreItems(bfi.keys,bfi,min=1,max=6) #specify the minimum and maximum values
scores
#> Call: psych::scoreItems(keys = bfi.keys, items = bfi, min = 1, max = 6)
#>
#> (Unstandardized) Alpha:
#> agree conscientious extraversion neuroticism openness
#> alpha 0.7 0.72 0.76 0.81 0.6
#>
#> Standard errors of unstandardized Alpha:
#> agree conscientious extraversion neuroticism openness
#> ASE 0.014 0.014 0.013 0.011 0.017
#>
#> Average item correlation:
#> agree conscientious extraversion neuroticism openness
#> average.r 0.32 0.34 0.39 0.46 0.23
#>
#> Median item correlation:
#> agree conscientious extraversion neuroticism openness
#> 0.34 0.34 0.38 0.41 0.22
#>
#> Guttman 6* reliability:
#> agree conscientious extraversion neuroticism openness
#> Lambda.6 0.7 0.72 0.76 0.81 0.6
#>
#> Signal/Noise based upon av.r :
#> agree conscientious extraversion neuroticism openness
#> Signal/Noise 2.3 2.6 3.2 4.3 1.5
#>
#> Scale intercorrelations corrected for attenuation
#> raw correlations below the diagonal, alpha on the diagonal
#> corrected correlations above the diagonal:
#> agree conscientious extraversion neuroticism openness
#> agree 0.70 0.36 0.63 -0.245 0.23
#> conscientious 0.26 0.72 0.35 -0.305 0.30
#> extraversion 0.46 0.26 0.76 -0.284 0.32
#> neuroticism -0.18 -0.23 -0.22 0.812 -0.12
#> openness 0.15 0.19 0.22 -0.086 0.60
#>
#> Average adjusted correlations within and between scales (MIMS)
#> agree cnscn extrv nrtcs opnns
#> agree 0.32
#> conscientious 0.22 0.34
#> extraversion 0.44 0.26 0.39
#> neuroticism -0.20 -0.26 -0.28 0.46
#> openness 0.11 0.15 0.18 -0.08 0.23
#>
#> Average adjusted item x scale correlations within and between scales (MIMT)
#> agree cnscn extrv nrtcs opnns
#> agree 0.68
#> conscientious 0.18 0.69
#> extraversion 0.33 0.19 0.71
#> neuroticism -0.14 -0.18 -0.17 0.76
#> openness 0.10 0.12 0.14 -0.05 0.62
#>
#> In order to see the item by scale loadings and frequency counts of the data
#> print with the short option = FALSE
#show the use of the keys.lookup with a dictionary
psych::keys.lookup(bfi.keys,bfi.dictionary[,1:4])
#> ItemLabel Item Giant3
#> A1- q_146 Am indifferent to the feelings of others. Cohesion
#> A2 q_1162 Inquire about others' well-being. Cohesion
#> A3 q_1206 Know how to comfort others. Cohesion
#> A4 q_1364 Love children. Cohesion
#> A5 q_1419 Make people feel at ease. Cohesion
#> C1 q_124 Am exacting in my work. Stability
#> C2 q_530 Continue until everything is perfect. Stability
#> C3 q_619 Do things according to a plan. Stability
#> C4- q_626 Do things in a half-way manner. Stability
#> C5- q_1949 Waste my time. Stability
#> E1- q_712 Don't talk a lot. Plasticity
#> E2- q_901 Find it difficult to approach others. Plasticity
#> E3 q_1205 Know how to captivate people. Plasticity
#> E4 q_1410 Make friends easily. Plasticity
#> E5 q_1768 Take charge. Plasticity
#> N1 q_952 Get angry easily. Stability
#> N2 q_974 Get irritated easily. Stability
#> N3 q_1099 Have frequent mood swings. Stability
#> N4 q_1479 Often feel blue. Stability
#> N5 q_1505 Panic easily. Stability
#> O1 q_128 Am full of ideas. Plasticity
#> O2- q_316 Avoid difficult reading material. Plasticity
#> O3 q_492 Carry the conversation to a higher level. Plasticity
#> O4 q_1738 Spend time reflecting on things. Plasticity
#> O5- q_1964 Will not probe deeply into a subject. Plasticity
#> Big6
#> A1- Agreeableness
#> A2 Agreeableness
#> A3 Agreeableness
#> A4 Agreeableness
#> A5 Agreeableness
#> C1 Conscientiousness
#> C2 Conscientiousness
#> C3 Conscientiousness
#> C4- Conscientiousness
#> C5- Conscientiousness
#> E1- Extraversion
#> E2- Extraversion
#> E3 Extraversion
#> E4 Extraversion
#> E5 Extraversion
#> N1 Emotional Stability
#> N2 Emotional Stability
#> N3 Emotional Stability
#> N4 Emotional Stability
#> N5 Emotional Stability
#> O1 Openness
#> O2- Openness
#> O3 Openness
#> O4 Openness
#> O5- Openness